DOSE AND DOSE-RATE EFFECTIVENESS FACTORS

FOR LOW-LET RADIATION

FOR APPLICATION TO NIOSH-IREP

John R. Trabalka,a A. Iulian Apostoaei, F. Owen Hoffman, Brian A. Thomas, and

David C. Kocher 

Oak Ridge Center for Risk Analysis, Inc.b

June 2017 

a Deceased.

b

 102 Donner Drive, Oak Ridge, TN 37830. Phone: (865) 483-6111; fax: (865) 481-0060; e-mail:

orrisk@orrisk.com. Research sponsored by The National Institute for Occupational Safety and Health 

(NIOSH) under contract with Oak Ridge Center for Risk Analysis, Inc. 
ii

DEDICATION

The co-authors of this report dedicate this comprehensive review of the state of knowledge of 

DDREFs to the memory and legacy of its principal author, Dr. John R. Trabalka, whose untimely death 

on February 23, 2014, occurred prior to completion of the final manuscript. Dr. Trabalka’s keen intellect, 

broad knowledge of radiation physics and biology, and relentless pursuit of relevant information were the 

driving forces behind this work.
iii

ACRONYMS AND ABBREVIATIONS

AECL Atomic Energy of Canada Limited

ALL Acute lymphocytic leukemia

AML Acute myelogenous leukemia; acute myeloid leukemia

ATB At time of bombings (Hiroshima and Nagasaki) 

ATM Ataxia Telangiectasia Mutated (protein kinase) 

BEIR Biological Effects of Ionizing Radiation (Committee of National Research Council)

bp Base pairs

CERRIE Committee Examining Radiation Risks of Internal Emitters (U.K.)

CI Confidence interval

CIRRPC Committee on Interagency Radiation Research and Policy Coordination 

CLL Chronic lymphocytic leukemia 

CML Chronic myelogenous leukemia; chronic myeloid leukemia 

CT Computed tomography 

d Day

DDREF Dose and dose-rate effectiveness factor

DHHS U.S. Department of Health and Human Services

DNA Deoxyribonucleic acid

DOE U.S. Department of Energy 

DREF Dose-rate effectiveness factor

DS02 Dosimetry System 2002 (Japanese atomic-bomb survivors) 

DS86 Dosimetry System 1986 (Japanese atomic-bomb survivors) 

DSB Double-strand break

EAR Excess absolute rate

EEOICPA Energy Employees Occupational Illness Compensation Program Act 

EPA U.S. Environmental Protection Agency 

EPRI Electric Power Research Institute

ERR Excess relative risk

eV Electron volt

FISH Fluorescence in situ hybridization 

G Giemsa (G-band, G-banding) 

GM Geometric mean

GSD Geometric standard deviation
iv

Gy Gray

h Hour 

HM Harmonic mean

hgprt Hypoxanthine-guanine phosphorobosyl transferase

HPA Health Protection Agency (U.K.) 

HPRT Hypoxanthine phosphorobosyl transferase

HR Homologous recombination 

IAEA International Atomic Energy Agency 

IARC International Agency for Research on Cancer

ICRP International Commission on Radiological Protection 

ICRU International Commission on Radiation Units and Measurements

INWORKS International Nuclear WORKers Study 

IREP Interactive RadioEpidemiological Program

kerma Kinetic energy released per unit mass

kVp Peak kilovoltage (x-ray tubes)

LD50 Lethal dose from ingestion in 50% of test sample 

LDEF Low-dose effectiveness factor

LET Linear energy transfer

LNT Linear no-threshold (dose-response model) 

LQ Linear-quadratic (dose-response model) 

LQE Linear-quadratic-exponential (dose-response model)

LSS Life Span Study (Japanese atomic-bomb survivors) 

mFISH Multifluor fluorescence in situ hybridization 

min Minute

MLE Maximum likelihood estimate

nc Non-coding 

NCRP National Council on Radiation Protection and Measurements 

NHEJ Non-homologous end-joining 

NIH National Institutes of Health

NIOSH National Institute for Occupational Safety and Health

NRC National Research Council

NRPB National Radiological Protection Board (U.K.) 

NRRW National Registry for Radiation Workers (U.K.)

ORNL Oak Ridge National Laboratory 
v

P P-value: probability of obtaining result equal to or more extreme than actually observed, 

 assuming that null hypothesis is true

PC/AS Probability of causation/assigned share 

RBE Relative biological effectiveness

RBEM Maximal RBE at low doses 

REF Radiation effectiveness factor

REFL REF at low doses or low dose rates

RERF Radiation Effects Research Foundation (Hiroshima, Japan) 

RNA Ribonucleic acid

RR Risk ratio

SE Standard error

SSA Single-strand annealing

Sv Sievert

TRDS Techa River Dosimetry System

UNSCEAR United Nations Scientific Committee on the Effects of Atomic Radiation

UV Ultraviolet (radiation)

y Year
vi

CONTENTS

EXTENDED SUMMARY ....................................................................................................................... xviii

 INTRODUCTION ............................................................................................................................. xviii

 USE OF LINEAR-QUADRATIC DOSE-RESPONSE MODEL TO ESTIMATE DDREFS ............. xx 

 UNCERTAINTY IN LOW-DOSE RESPONSES DUE TO COMPLEXITY OF

 BIOLOGICAL SYSTEMS AND RESPONSE MECHANISMS ........................................................ xxi 

 RECENT CHALLENGES TO LNT MODEL ..................................................................................... xxi 

 GENERAL CONSIDERATIONS IN RE-EVALUATING DDREFS IN IREP ................................. xxii

 EVALUATIONS OF DATA TO ESTIMATE DDREFS .................................................................. xxiv 

 Evaluations of Data from Radiobiological Studies ...................................................................... xxiv 

 Genetic and cytogenetic data ................................................................................................. xxiv 

 Cancer induction in animals ................................................................................................... xxiv 

 Life shortening in animals ....................................................................................................... xxv 

 Summary of data from studies in animals ............................................................................... xxv 

 Evaluations of Data from Epidemiological Studies ..................................................................... xxvi 

 Evaluation of data for solid cancers ...................................................................................... xxvii

 Evaluation of data for leukemias .......................................................................................... xxxii

 RECOMMENDATION ON PROBABILITY DISTRIBUTION OF DDREF FOR SOLID

 CANCERS ....................................................................................................................................... xxxiv 

 Assumptions and Initial Conclusions ......................................................................................... xxxiv 

 Development of Probability Distribution of DDREF for All Solid Cancers .............................. xxxv 

1. INTRODUCTION ................................................................................................................................... 1 

 1.1 BACKGROUND .............................................................................................................................. 1 

 1.1.1 Probability Distribution of DDREF in IREP .......................................................................... 3 

 1.1.2 Probability Distributions of DDREF Developed by Other Investigators ................................ 4 

 1.2 PURPOSE AND APPROACH ......................................................................................................... 6 

2. DERIVATION OF DDREF ................................................................................................................... 10 

 2.1 INTRODUCTION .......................................................................................................................... 10 

 2.2 METHODS USED TO DERIVE DDREF ...................................................................................... 11 

 2.3 APPLICABILITY OF LINEAR-QUADRATIC DOSE-RESPONSE MODEL FOR

 CANCER ........................................................................................................................................ 13 

 2.4 COMPLICATIONS IN ESTIMATING DDREF............................................................................ 18 

 2.4.1 Joint Effects of Cell Sterilization, Dose, and Dose Rate on Dose-Responses ...................... 18 
vii

 2.4.1.1 Representation of effects of cell sterilization ........................................................... 18 

 2.4.1.2 Impact of cell sterilization on estimation of LDEF .................................................. 19 

 2.4.1.3 Impact of cell sterilization on estimation of DREF ................................................. 21 

 2.4.1.4 Influence of dose rate on estimation of slope of dose-response at low doses .......... 22 

 2.4.1.5 Summary and conclusions........................................................................................ 23 

 2.4.2 Potential Energy Dependence of RBE and DDREF for Low-LET Radiations ..................... 24 

 2.4.2.1 Background .............................................................................................................. 24 

 2.4.2.2 Significance .............................................................................................................. 25 

 2.4.2.3 Summary of efforts to quantify energy dependence of RBE ................................... 26 

 2.4.2.4 Recent challenges of data on energy dependence of RBE and DDREF .................. 28 

 2.4.2.5 Current status and conclusions ................................................................................. 32 

 2.4.3 Effect of Uncertainty in RBE for Neutrons on Dose-Responses in LSS Cohort .................. 33 

 2.5 OVERALL SUMMARY AND CONCLUSIONS .......................................................................... 34 

3. STATUS OF LNT DOSE-RESPONSE MODEL AND POSSIBLE ALTERNATIVES ...................... 37 

 3.1 INTRODUCTION .......................................................................................................................... 37 

 3.2 REVIEW OF PHENOMENA INVOLVED IN RADIATION CARCINOGENESIS .................... 38 

 3.2.1 Initial Radiation Damage and Cellular Responses ................................................................ 38 

 3.2.2 Importance of Epigenetic Phenomena .................................................................................. 42 

 3.2.3 Effect of Ionizing Radiation on Carcinogenesis ................................................................... 43 

 3.2.4 Modulation of Carcinogenesis by Integrated Cellular and Multicellular Responses ............ 45 

 3.2.5 Effects of Differences in Gene Expression at Low Doses .................................................... 46 

 3.2.6 Conclusions ........................................................................................................................... 47 

 3.3 ALTERNATIVE MODELS OF LOW-DOSE RESPONSES ........................................................ 48 

 3.4 LNT MODEL─STILL THE DEFAULT? ...................................................................................... 56 

4. RADIOBIOLOGICAL STUDIES TO ESTIMATE DDREF ................................................................ 61 

 4.1 INTRODUCTION .......................................................................................................................... 61 

 4.2 STUDIES OF GENETIC AND CYTOGENETIC ENDPOINTS .................................................. 62 

 4.3 STUDIES IN WHOLE ANIMALS ................................................................................................ 68 

 4.3.1 Overview of Studies to Estimate DDREFs ........................................................................... 68 

 4.3.2 Influence of Biological Factors ............................................................................................. 73 

 4.3.3 Analyses of Data for Leukemia............................................................................................. 75 

 4.3.4 Consideration of Data for All Solid Tumors Combined ....................................................... 79 

 4.3.5 Analysis of Data for Mammary Tumors ............................................................................... 81 
viii

 4.3.5.1 Review of data ......................................................................................................... 81 

 4.3.5.2 Relevance of data in animals to breast cancer in humans ........................................ 88 

 4.3.6 Analysis of Data on Life Shortening ..................................................................................... 91 

 4.3.6.1 Review of data ......................................................................................................... 91 

 4.3.6.2 Influence of other biological factors ........................................................................ 95 

 4.3.6.3 Summary and conclusions on use data on life shortening to estimate DDREF ....... 97 

 4.4 SUMMARY AND CONCLUSIONS ON USE OF RADIOBIOLOGICAL DATA TO

 ESTIMATE DDREF ....................................................................................................................... 98 

5. EPIDEMIOLOGICAL STUDIES TO ESTIMATE DDREF .............................................................. 100 

 5.1 ADVANTAGES AND DISADVANTAGES OF VARIOUS EPIDEMIOLOGICAL

 DATA: IMPLICATIONS FOR ESTIMATING DDREF ............................................................. 100 

 5.1.1 Total Cancer vs Cancer-Specific Risks ............................................................................... 101 

 5.1.1.1 Use of data for all cancers to represent DDREFs for specific cancers .................. 101 

 5.1.1.2 Importance of risk transfer for all solid cancers vs specific cancers ..................... 103 

 5.1.2 Analysis of Cancer Incidence vs Mortality ......................................................................... 104 

 5.1.3 Conclusions ......................................................................................................................... 106 

 5.2 DDREFS DERIVED FROM DOSE-RESPONSES FOR ALL SOLID CANCERS .................... 106 

 5.2.1 Analyses of Dose-Responses in LSS cohort ....................................................................... 106 

 5.2.1.1 Influence of definition of LSS cohort .................................................................... 116 

 5.2.1.2 Influence of doses from exposure to fallout and neutron activation products ....... 121 

 5.2.1.3 Effects of uncertainty in RBE for neutrons ............................................................ 124 

 5.2.1.4 Effects of dose-range analyzed and formulation of dose-response model ............. 128 

 5.2.1.5 Effects of uncertainties in LSS data and applicability of model parameters.......... 133 

 5.2.1.6 Effects of combining data for individual solid cancers .......................................... 137 

 5.2.1.7 Conclusions ............................................................................................................ 139 

 5.2.2 Epidemiological Studies of Chronic or Protracted Exposures ............................................ 140 

 5.2.2.1 Studies of Mayak workers ...................................................................................... 141

 5.2.2.2 Studies of cohorts of western nuclear workers....................................................... 142 

 5.2.2.2.1 Limitations of analyses of risks to workers ............................................ 147 

 5.2.2.2.2 Derivation of DREFs based on recent estimates of risks to

 workers and LSS cohort ......................................................................... 151 

 5.2.2.3 Studies of Techa River cohort ................................................................................ 153 

 5.2.2.4 Analysis of multiple studies by Jacob et al. (2009) ................................................ 156 

 5.2.2.4.1 Description of analysis and presentation of results ............................... 156 
ix

 5.2.2.4.2 Critique of analysis ................................................................................ 160

 5.2.2.5 Conclusions ............................................................................................................ 164

 5.3 STUDIES OF FEMALE BREAST CANCER .............................................................................. 165 

 5.3.1 Effects of Dose Rate in Analysis of Data on Breast Cancer Incidence by Preston et

 al. (2002) ............................................................................................................................. 167 

 5.3.2 Potential Influence of Energy Dependence of REFL for Photons on Analysis of Data

 on Breast Cancer Incidence by Preston et al. (2002) .......................................................... 172 

 5.3.3 Analysis of Breast Cancer Mortality in Canadian Tuberculosis Fluoroscopy Cohorts. ..... 178 

 5.3.4 Analysis of Breast Cancer Incidence in Techa River Cohort .............................................. 180 

 5.3.5 Discussion of Studies of Breast Cancer .............................................................................. 181 

 5.4 STUDIES OF THYROID CANCER ............................................................................................ 183 

 5.4.1 Risks from Exposure to External Radiation ........................................................................ 185 

 5.4.2 Risks from Internal Exposure to Radioiodine ..................................................................... 187 

 5.4.3 Effects of Dose Fractionation or Protraction on Estimates of DDREF ............................... 189 

 5.4.4 Updated Analysis of Risks of Thyroid Cancer from Childhood Exposures ....................... 190 

 5.4.5 Potential Influence of Energy Dependence of REFL on Estimates of DDREF ................... 191 

 5.4.6 Influence on DDREF of Uncertainty in Risks in Israeli Tinea Capitis Cohort ................... 195 

 5.4.7 Discussion of Studies of Thyroid Cancer............................................................................ 196 

 5.5 STUDIES OF LUNG CANCER ................................................................................................... 198 

 5.5.1 Incidence of Lung Cancer in LSS Cohort: Complication by Effects of Smoking .............. 198 

 5.5.2 Mortality from Lung Cancer in Mayak Workers ................................................................ 200 

 5.5.3 Mortality from Lung Cancer in Western Nuclear Workers ................................................ 201 

 5.5.4 Mortality from Lung Cancer in Tuberculosis Fluoroscopy Cohorts ................................... 202 

 5.5.5 Conclusions from Studies of Lung Cancer ......................................................................... 204 

 5.6 STUDIES OF NON-MELANOMA SKIN CANCERS ................................................................ 205 

 5.6.1 Recent Epidemiological Studies ......................................................................................... 205 

 5.6.2 Conclusions from Studies of Skin Cancer .......................................................................... 208 

 5.7 STUDIES OF BONE CANCER ................................................................................................... 208 

 5.8 STUDIES OF LEUKEMIA .......................................................................................................... 209 

 5.8.1 Introduction ......................................................................................................................... 209 

 5.8.2 Analysis of Data on Leukemia in LSS Cohort .................................................................... 210 

 5.8.3 Data on Leukemia in Chronically Exposed Cohorts ........................................................... 218 

 5.8.3.1 Western nuclear workers ........................................................................................ 218 

 5.8.3.2 Mayak workers ....................................................................................................... 221 

 5.8.3.3 Techa River cohort ................................................................................................. 221 
x

 5.8.3.4 Meta-Analysis by Daniels and Schubauer-Berigan (2011) .................................... 223 

 5.8.3.5 Study of U.S. nuclear workers by Daniels et al. (2013) ......................................... 228 

 5.8.3.6 Studies of childhood leukemia from exposure to natural background radiation .... 230 

 5.8.4 Summary of Analyses of Data on Leukemia ...................................................................... 232 

 5.9 SUMMARY OF EPIDEMIOLOGICAL DATA AND CONCLUSIONS .................................... 234 

6. DEVELOPMENT OF PROBABILITY DISTRIBUTION OF DDREF FOR ALL SOLID

 CANCERS ........................................................................................................................................... 247 

 6.1 INTRODUCTION ........................................................................................................................ 247 

 6.2 DOSES AND DOSE RATES WHERE DDREF SHOULD BE APPLIED ................................. 250 

 6.2.1 Defining Probability Distribution of Upper Limit of Low Acute Dose .............................. 252 

 6.2.2 Defining Upper Limit of Low Dose Rate ........................................................................... 254 

 6.3 DEVELOPMENT OF DDREF DISTRIBUTION BASED ON EPIDEMIOLOGICAL

 DATA ........................................................................................................................................... 254 

 6.3.1 Selection and Prioritization of Epidemiological Data ......................................................... 254 

 6.3.2 Development of Preferred Probability Distribution of DDREF .......................................... 261 

 6.3.2.1 Description of procedure; assumptions and results for base-case scenario ............ 261 

 6.3.2.2 Sensitivity analysis ................................................................................................. 266 

 6.3.2.3 Discussion of approach to obtaining preferred DDREF distribution ..................... 270 

 6.3.2.4 Discussion of alternative approaches to combining distributions of LDEF or 

 DREF ..................................................................................................................... 271 

 6.3.3 Depiction of Preferred Probability Distribution of DDREF and Lognormal Equivalent .... 273 

 6.3.4 Summary of Preferred Probability Distribution of DDREF; Importance of Harmonic 

 Mean ................................................................................................................................... 273 

 6.3.5 Comparison of Preferred Probability Distribution of DDREF with Distributions in

 IREP .................................................................................................................................... 275 

 6.4 CONSIDERATIONS ON USE OF RISK RATIO IN CANCER RISK ASSESSMENTS .......... 276 

 6.5 SUMMARY AND CONCLUSIONS ........................................................................................... 277

APPENDIX A. DOSES AND DOSE RATES FOR APPLICATION OF DDREFS .............................. 283 

 A.1 BACKGROUND ......................................................................................................................... 283 

 A.2 SPECIFICATION OF LOW DOSE FROM ACUTE EXPOSURE ............................................ 283 

 A.2.1 Recommendations by Expert Groups ................................................................................ 286 

 A.2.2 Microdosimetric Considerations ....................................................................................... 287 

 A.2.3 Analyses of Data in Cells .................................................................................................. 291 

 A.2.4 Analyses of Data in Laboratory Animals .......................................................................... 292 
xi

 A.2.5 Analyses of Epidemiological Data .................................................................................... 292 

 A.2.5.1 Analyses of data in LSS cohort ........................................................................... 293 

 A.2.5.2 Information from other epidemiological studies ................................................. 294 

 A.2.6 Conclusions About Upper Limit of Low Acute Dose ....................................................... 295 

 A.3 SPECIFICATION OF LOW DOSE RATE ................................................................................. 296 

 A.3.1 Recommendations by Expert Groups ................................................................................ 296 

 A.3.2 Data on Cancer Induction in Laboratory Animals ............................................................ 298 

 A.3.3 Analyses of Data in Cells .................................................................................................. 299 

 A.3.4 Microdosimetric Considerations ....................................................................................... 300 

 A.3.5 Epidemiological Study of Childhood Leukemias ............................................................. 300 

 A.3.6 Conclusions About Upper Limit of Low Dose Rate ......................................................... 301 

APPENDIX B. ESTIMATION OF DDREF IN BEIR VII REPORT ..................................................... 302 

 B.1 APPROACH TO ESTIMATION OF DDREF BY BEIR VII COMMITTEE ............................. 302 

 B.2 CRITIQUE OF APPROACH IN BEIR VII REPORT ................................................................. 306 

 B.2.1 Basic Assumptions ............................................................................................................ 306 

 B.2.2 Influence of Biological Factors on Tumor Responses....................................................... 307 

 B.2.3 Use of Life-Span Shortening As Endpoint for Solid Tumor Incidence............................. 310 

 B.2.4 Application of Animal Data in BEIR VII Committee’s Bayesian Analysis ..................... 312 

 B.2.5 Summary and Conclusions ................................................................................................ 313 

REFERENCES ......................................................................................................................................... 315 
xii

TABLES

ES.1 Estimates of 50th percentiles and 90% CIs of LDEFs and DREFs for all solid cancers

 included in analysis to develop probability distribution of DDREF .............................................. xxix 

ES.2 Summary of probability distribution of DDREF for all solid cancers developed in this 

 report and lognormal distribution that gives best fit to DDREF distribution ............................. xxxviii

2.1 Medians and ranges of central estimates of dose-response parameters, LDEFs, and 

 maximum RBEs at low doses (RBEM) for induction of dicentric chromosome aberrations 

 in human lymphocytes by 220–250 kVp x rays relative to 60Co gamma rays ................................... 26 

2.2 Dose-response parameters and median and range of central estimates of maximum RBEs

 at low doses (RBEM) for induction of dicentric chromosome aberrations in human 

 lymphocytes by 250 kVp x rays delivered at doses ≤0.3 Gy relative to 60Co gamma rays ............... 27 

2.3 Dose-response parameters and LDEFs for induction of exchange aberrations in human 

 lymphocytes and fibroblasts by acute exposure to 137Cs gamma rays from studies using

 mFISH ................................................................................................................................................ 29 

2.4 Dose-response parameters, LDEFs, and maximum RBEs at low doses (RBEM) for induction 

 of apparently simple translocations in human lymphocytes by x rays relative to 60Co gamma

 rays from studies using FISH ............................................................................................................. 30 

4.1 Estimates of DREF and LDEF based on dose-responses for genetic and cytogenetic

 endpoints obtained mainly from in vitro studies of mutagenesis, cell transformation, and

 chromosome aberrations induced by low-LET radiation ................................................................... 63 

4.2 Estimates of DREF based on dose-responses for cancer induction in laboratory animals by 

 high-energy gamma radiation ............................................................................................................ 70 

4.3 Incidence rate (% ± 1 SE) of mammary adenocarcinomas in BALB/c mice from acute,

 fractionated, or protracted exposure to 137Cs gamma rays ................................................................. 84 

4.4 DREFs for mammary carcinomas in rats induced by low-LET radiation .......................................... 88 

5.1 LDEFs derived from analyses of curvature in modeled dose-responses (ERRs) for

 incidence of all solid cancers in LSS cohort based on DS86 dosimetry .......................................... 107 

5.2 LDEFs derived from analyses of curvature in modeled dose-responses (ERRs) for

 incidence of all solid cancers in LSS cohort based on DS02 dosimetry .......................................... 108 

5.3 LDEFs derived from analyses of curvature in modeled dose-responses (ERRs) for

 mortality from all solid cancers in LSS cohort based on DS86 dosimetry ...................................... 109 

5.4 LDEFs derived from analyses of curvature in modeled dose-responses (ERRs or EARs)

 for mortality from all solid cancers in LSS cohort based on DS02 dosimetry ................................. 110 
xiii

5.5 LDEFs derived from analyses of curvature in modeled dose-responses (ERRs) for

 incidence of all solid cancers in LSS cohort based on DS86 dosimetry and different 

 assumptions about neutron RBE ...................................................................................................... 125 

5.6 LDEFs derived from analyses of curvature in modeled dose-responses (ERRs) with

 dependence on age at exposure for mortality from all solid cancers in LSS cohort based

 on DS86 dosimetry and different assumptions about dose specification and neutron RBE ............ 126 

5.7 LDEFs derived from analyses of curvature in modeled dose-responses (ERRs) with

 dependence on attained age for mortality from all solid cancers in LSS cohort based on

 DS86 dosimetry and different assumptions about dose specification and neutron RBE ................. 126 

5.8 LDEFs derived from analyses of curvature in modeled dose-response (ERRs) for mortality

 from all solid cancers in LSS cohort based on DS02 dosimetry and different assumptions 

 about neutron RBE ........................................................................................................................... 127 

5.9 Estimates of ERR/Sv for cancer mortality in selected studies of radiation workers and LSS

 cohort ............................................................................................................................................... 143 

5.10 Estimates of ERR/Sv or ERR/Gy (and 90% CIs) for cancer mortality or incidence from 

 selected studies of radiation workers and LSS cohort ..................................................................... 145 

5.11 DREFs derived by comparing estimates of ERR/Sv or ERR/Gy for all cancers excluding

 leukemia or all solid cancers from selected studies of radiation workers with matched

 estimates of ERR/Gy in LSS cohort based on DS02 dosimetry ...................................................... 152 

5.12 Risk ratios derived by comparing estimates of ERR/Gy for cancer mortality in nine cohorts 

 that received chronic or protracted exposures with matched estimates in LSS cohort based 

 on DS02 dosimetry .......................................................................................................................... 158 

5.13 Risk ratios derived by comparing estimates of ERR/Gy for cancer incidence in three cohorts 

 that received chronic or protracted exposures with matched estimates in LSS cohort based 

 on DS02 dosimetry .......................................................................................................................... 159 

5.14 LDEFs (and 95% CIs) derived from analyses of curvature in dose-responses (ERRs) for 

 incidence of various solid cancers in LSS cohort based on DS86 dosimetry .................................. 166 

5.15 Estimates of ERR and EAR (and 95% CIs) for incidence of female breast cancer from full

 pooled analysis of eight cohort studies of external exposure to low-LET radiation ........................ 168 

5.16 Estimates of ERR and EAR (and 95% CIs) for incidence of female breast cancer from

 simple pooled analysis of eight cohort studies of external exposure to low-LET radiation ............ 170 

5.17 DREFs (50th percentiles and 90% CIs) derived by comparing estimates of ERR and EAR

 for breast cancer incidence in four cohort studies of highly fractionated or chronic exposures 

 with matched estimates in LSS cohort based on DS86 dosimetry and accounting for increased

 biological effectiveness (REFL) of medical x rays compared with high-energy gamma rays ......... 175 

5.18 Estimates of ERR and EAR (and CIs) for thyroid cancer incidence from cohort studies of 

 external exposure to low-LET radiation before age 15 .................................................................... 184 
xiv

5.19 Estimates of ERR and EAR (and 95% CIs) for thyroid cancer incidence in LSS cohort 

 based on DS02 dosimetry that can be compared with estimates in cohorts of medical

 patients that received external exposure to low-LET radiation........................................................ 188 

5.20 DDREFs (50th percentiles and 90% CIs) derived by comparing estimates of ERR and EAR

 for thyroid cancer incidence in five cohort studies of fractionated or chronic exposures with

 matched estimates in LSS cohort based on DS02 dosimetry and accounting for increased

 biological effectiveness (REFL) of medical x rays compared with high-energy gamma rays ......... 193 

5.21 LDEFs derived from analyses of curvature in modeled dose-responses (EARs or ERRs) for

 incidence of all leukemias in LSS cohort based on DS86 or DS02 dosimetry ................................ 212 

5.22 LDEFs derived from analyses of curvature in modeled dose-responses (EARs or ERRs) for

 mortality from all leukemias in LSS cohort based on DS86 or DS02 dosimetry ............................ 213 

5.23 Estimates of ERRs (and CIs) from studies of leukemia mortality or incidence from chronic 

 or protracted exposures included in analysis by Daniels and Schubauer-Berigan (2011) ............... 224 

5.24 Estimates of ERRs (and CIs) from meta-analysis of studies of leukemia mortality or

 incidence from chronic or protracted exposures by Daniels and Schubauer-Berigan (2011) .......... 226 

5.25 Estimates of ERRs (and 95% CIs) based on linear dose-responses for leukemia mortality in

 U.S. nuclear workers using exposure lag models ............................................................................ 229 

5.26 Summary of estimates of DDREF based on epidemiological studies of solid cancers .................... 235 

5.27 Summary of estimates of DDREF based on epidemiological studies of leukemias ........................ 239 

6.1 Estimates of 50th percentiles and 90% CIs of LDEFs and DREFs for all solid cancers

 included in analysis to develop probability distribution of DDREF ................................................ 256 

6.2 Percentiles of probability distributions of LDEF, DREF, and DDREF for all solid cancers 

 calculated using stepwise procedure for combining probability distributions of LDEF or 

 DREF from epidemiological studies to obtain preferred DDREF distribution ................................ 267 

6.3 Percentiles of probability distributions of DDREF for all solid cancers based on alternative

 scenarios for combining probability distributions of LDEF or DREF from epidemiological 

 studies compared with preferred DDREF distribution ..................................................................... 269 

6.4 Summary of preferred probability distribution of DDREF for all solid cancers and 

 lognormal distribution that gives best fit to DDREF distribution .................................................... 275 

6.5 Summary of probability distribution of risk ratio for all solid cancers corresponding to 

 preferred probability distribution of DDREF and lognormal distribution that gives best fit to

 distribution of risk ratio.................................................................................................................... 279 

A.1 Estimates of upper limit of low dose from acute exposure to low-LET radiation ............................ 284 

A.2 Estimates of upper limit of low dose rate from protracted exposure to low-LET radiation ............. 297 
xv

FIGURES

ES.1 Estimates of 50th percentiles and 90% CIs of DREF, LDEF, or DDREF for all solid cancers

 or specific solid cancers based on selected epidemiological studies ........................................... xxviii

ES.2 Estimates of 50th percentiles and 90% CIs of DREF or LDEF for solid cancer incidence or

 mortality used to develop probability distribution of DDREF for all solid cancers in this

 report and comparison of our preferred distribution with DDREF distributions developed by 

 Jacob et al. (2009) and BEIR VII committee (NRC 2006) and DDREF distribution for most 

 solid cancers currently used in IREP ........................................................................................... xxxix 

1.1 Schematic representation of different dose-response relationships ..................................................... 2 

1.2 Discrete probability distributions of DDREF used in IREP ................................................................ 3 

1.3 Probability distributions developed by different investigators to represent subjective

 uncertainty in DDREF ......................................................................................................................... 5 

2.1 Representation of data on dicentric chromosome aberration frequency vs absorbed dose in

 human lymphocytes to illustrate dependence of low-dose effectiveness factor (LDEF) on 

 dose assuming linear-quadratic (LQ) dose-response ......................................................................... 12 

2.2 Exchange breakpoints per cell vs dose from acute or chronic exposure measured using

 mFISH ................................................................................................................................................ 17 

2.3 Representations of radiation dose-responses for cancer incidence .................................................... 20 

2.4 Measured probability distributions of dose vs lineal energy (y) in 1-μm-diameter sphere of 

 tissue-equivalent material for different types of incident radiation ................................................... 25 

2.5 Dose-responses for lifetime incidence of myeloid leukemia in two strains of male mice

 from acute exposure to different low-LET radiations ........................................................................ 31 

3.1 Events initiated by DNA damage-monitoring system and DNA-damage-independent 

 processes involving plasma membrane in response to exposure to ionizing radiation ...................... 39 

3.2 Diagram of high- and low-LET radiation tracks in section of chromatin .......................................... 40 

3.3 Representation of cellular and tissue-level response mechanisms to deal with DNA DSBs

 induced by ionizing adiation .............................................................................................................. 45 

4.1 Incidence of mammary adenocarcinomas in female BALB/c mice from acute exposure to

 fission neutrons or 137Cs gamma rays ................................................................................................ 83 

4.2 Effect of dose rate and dose fractionation on incidence of mammary carcinomas in female

 BALB/c mice exposed to 137Cs gamma rays ...................................................................................... 83 

4.3 Effect of dose rate on fractional mortality rate from all causes except leukemia and

 lymphoma in B6CF1 and LAF1 mice exposed daily over a lifetime to 60Co gamma rays .................. 92 
xvi

5.1 Estimates of ERR/Gy for major causes of death in LSS cohort based on DS02 dosimetry ............ 102 

5.2 Estimates of relative risks of incidence of all solid cancers in LSS cohort at neutron-weighted

 colon doses <2 Sv based on DS86 dosimetry with distal survivors beyond 3 km omitted .............. 117 

5.3 Dose-responses (ERRs) for mortality from all solid cancers in LSS cohort from reanalyses of

 data at neutron-weighted colon doses of 0–2.5 Sv based on DS02 and DS86 dosimetry ................ 118 

5.4 ERR/Gy ±1 SE for incidence of all solid cancers in proximal survivors in LSS cohort with

 neutron-weighted colon doses <2 Gy based on DS86 dosimetry, follow-up for period 

 1958–1994, and linear regression of responses over dose intervals from zero to selected dose ...... 120 

5.5 ERR/Gy ±1 SE for incidence of all solid cancers in LSS cohort, including distal survivors,

 with neutron-weighted colon doses <2 Gy based on DS86 dosimetry, follow-up for period 

 1958–1994, and linear regression of responses over dose intervals from zero to selected dose ...... 120 

5.6 ERR/Gy and 95% CIs for mortality from all solid cancers in LSS cohort, including distal

 survivors, with neutron-weighted colon doses <2 Gy based on DS02 dosimetry, follow-up 

 through 2003, and analyses over selected dose ranges based on linear dose-response model ......... 121 

5.7 Illustration of approach by BEIR VII committee to estimating DDREF (LDEF) based on

 data for solid cancer incidence in LSS cohort .................................................................................. 129 

5.8 Dose-response (ERRs) and 90% CIs for solid cancers in LSS cohort based on data for period

 1958–1997, neutron-weighted colon doses of 0–5 Gy, and DS86 dosimetry .................................. 131 

5.9 Dose-response (ERRs) and 95% CIs for mortality from all solid cancers in LSS cohort based

 on data for period 1950–2003 and DS02 dosimetry with linear-quadratic (LQ) and linear (L)

 fits over full range of neutron-weighted colon doses corresponding to shielded kerma <4 Gy 

 and LQ fit at colon doses of 0–2 Gy ................................................................................................ 132 

5.10 Alternate representations of effects of age at exposure on excess risks (ERRs and EARs) of 

 incidence of all solid cancers in LSS cohort based on DS02 dosimetry .......................................... 136 

5.11 Dose-responses (ERRs) for incidence of specific solid cancers in LSS cohort at attained

 age 70 after exposure at age 30 based on DS02 dosimetry .............................................................. 138 

5.12 Pooled dose-responses (relative risks) for incidence of thyroid cancer and fitted curves based

 on five cohort studies of childhood (age <15 y) exposure to external x or gamma radiation .......... 186 

5.13 Dose-response (ERRs) for incidence of all non-melanoma skin cancers in LSS cohort based

 on DS02 dosimetry .......................................................................................................................... 206 

5.14 Relative risks of leukemia mortality and incidence in LSS cohort estimated by Little and

 Muirhead (1998) based on DS86 dosimetry .................................................................................... 210 

5.15 Excess cases (EARs) of leukemia mortality in LSS cohort based on DS86 or DS02 dosimetry

 estimated by Preston et al. (2004). ................................................................................................... 211 

5.16 Dose-responses (ERRs) for incidence of specific types of leukemia in LSS cohort based on 

 DS02 dosimetry ............................................................................................................................... 217 
xvii

6.1 Dependence of DDREF for all solid cancers except breast and thyroid under conditions of 

 acute exposure to low-LET radiations, DDREFacute, on dose for assumed DDREF under 

 conditions of chronic exposure, DDREFchronic, and reference dose, DL, above which acute 

 dose-response is assumed to be linear .............................................................................................. 252 

6.2 Estimates of 50th percentiles and 90% CIs of DREF, LDEF, or DDREF for all solid cancers

 or specific solid cancers based on epidemiological studies ............................................................. 257 

6.3 Estimates of DREF, LDEF, or DDREF and ranges or CIs based on dose-responses for 

 various cancers and life shortening in laboratory animals ............................................................... 262 

6.4 Representations of preferred DDREF distribution for all solid cancers as probability

 (frequency) distribution and log-probability plot ............................................................................. 274 

6.5 Representations of risk ratio corresponding to preferred DDREF distribution for all solid

 cancers as probability (frequency) distribution and log-probability plot ......................................... 278 

6.6 Estimates of 50th percentiles and 90% CIs of DREF or LDEF for solid cancer incidence or

 mortality used to develop probability distribution of DDREF for all solid cancers in this

 report and comparison of our preferred distribution with DDREF distributions developed by 

 Jacob et al. (2009) and BEIR VII committee (NRC 2006) and DDREF distribution for most 

 solid cancers currently used in IREP ............................................................................................... 281 

A.1 DNA double-strand break induction in normal human fibroblasts, based on mean values of 

 γ-H2AX foci counted 3 min after irradiation or data from pulsed-field gel electrophoresis

 analysis ............................................................................................................................................. 289 

A.2 Effect of dose and dose rate on frequency of translocations in mouse lymphocytes 

 following acute, chronic, or fractionated exposures to gamma rays in vivo .................................... 290 

B.1 Estimated risks of cancer induction in specific-pathogen-free m ce vs dose from acute

 exposure in studies conducted in barrier facility at Oak Ridge National Laboratory ...................... 303 

B.2 Data on life-span shortening due to radiation exposure used by BEIR VII committee ................... 312 
xviii

EXTENDED SUMMARY

INTRODUCTION

This report describes a study for The National Institute for Occupational Safety and Health 

(NIOSH) to re-evaluate dose and dose-rate effectiveness factors (DDREFs) for low-LET radiation 

(photons and electrons) that are incorporated in cancer risk models in the Interactive 

RadioEpidemiological Program (IREP). The objective of this study was to develop recommendations that 

provide unbiased representations of the current state of knowledge of DDREFs for low-LET radiation. 

NIOSH uses IREP to estimate the probability of causation/assigned share (PC/AS)1 of diagnosed 

cancers in nuclear energy workers who were exposed to ionizing radiation. IREP estimates PC/AS of a 

diagnosed cancer in an individual with known radiation exposures as PC/AS = ERR/(ERR + 1), where 

ERR is the excess relative risk of incidence of the individual’s cancer type associated with the known 

exposures. Models to estimate risks (ERRs) of cancer incidence in IREP are based primarily on studies of 

Japanese atomic-bomb survivors [the Life Span Study (LSS) cohort] who received an acute exposure 

mainly to high-energy photons, with a small contribution from neutrons, at doses to the colon up to about

4 Gy.2 DDREFs are incorporated in cancer risk models in IREP to account for the possibility that the

effectiveness of low-LET radiations in inducing cancer in humans at low doses or low dose rates differs 

from the effectiveness of those radiations at high doses and high dose rates. 

As in all recent analyses of cancer risks in the LSS cohort, assumptions in IREP about DDREFs for 

solid cancers and the dependence of risks of solid cancers on dose differ from the assumptions for 

leukemias. Risks of solid cancers from exposure to low-LET radiation are estimated in IREP by assuming 

a linear, no-threshold (LNT) dose-response model modified by a DDREF. The risk of a solid cancer per 

unit dose at low doses or low dose rates of low-LET radiation, RL, is estimated as RL = RH/DDREF, where 

RH is the risk per unit dose at high doses and high dose rates, as estimated primarily on the basis of fits to 

dose-responses in the LSS cohort assuming an LNT model. Two DDREFs for solid cancers are used in 

 

1 The version of IREP used by NIOSH refers to the estimated probability that a diagnosed cancer in an individual 

was caused by exposure to ionizing radiation as “probability of causation,” whereas the working group that 

developed IREP (Land et al. 2003a) preferred the term “assigned share” to indicate that the quantity calculated in 

IREP (1) is based on estimates of cancer risks obtained from epidemiological studies of exposed populations and 

(2) is a property of the population group to which an individual belongs that is assigned to that individual but may 

not be the true probability that an individual’s cancer was caused by known radiation exposures. 2 Doses to members of the LSS cohort are neutron-weighted doses, which usually are calculated as the sum of the 

absorbed dose from photons and 10 times the absorbed dose from neutrons to account for the greater biological

effectiveness of neutrons in inducing cancer in humans at high acute doses. The highest neutron-weighted doses to 

some organs (e.g., bone marrow, breast, thyroid) exceeded 4 Gy.
xix

IREP, one for breast and thyroid cancers and one for all other solid cancers. Both DDREFs are described 

by probability distributions to represent their uncertainty (Land et al. 2003a). 

In contrast to the approach to estimating risks of solid cancers, a DDREF is not used explicitly in

IREP in estimating risks of leukemias from exposure to low-LET radiation. In cases of acute exposure to 

low-LET radiation, risks of leukemias are estimated by assuming a linear-quadratic (LQ) dose-response 

model, which incorporates a DDREF >1 implicitly. In an LQ model, the risk, R, from acute exposure at 

dose D (Gy) is estimated as R = αD + βD2

, where α (Gy−1) and β (Gy−2) are the model coefficients. With 

this assumption, DDREF = RH/RL is a dose-dependent quantity given by (α + βD)/α = 1 + (β/α)D, where 

the quantity β/α, referred to as the curvature parameter, represents the extent to which the dose-response 

departs from linearity. In estimating risks of all leukemias (excluding chronic lymphocytic leukemia, 

CLL) or specific types of leukemia in IREP, the coefficients α and β in the LQ dose-response models are 

assumed to be equal, so that, for example, DDREF is about 2 at an acute dose of 1 Gy.3

 

In cases of chronic exposure to low-LET radiation, a DDREF is applied in IREP in estimating risks

of solid cancers at any total dose. In cases of acute exposure, a DDREF is applied in estimating risks of 

solid cancers only at doses below an uncertain dose, DL, in the range of 30–200 mGy. This range was 

intended to represent the uncertainty in radiobiological and epidemiological data that could be used to 

define a “low” acute dose, i.e., an acute dose below which a DDREF should be applied. At acute doses of 

low-LET radiation less than 30–200 mGy, the DDREFs for solid cancers in IREP are assumed to vary 

smoothly with dose, starting from the value 1 with no uncertainty at dose DL and reaching 99.9% of the 

DDREF for chronic exposure at a dose of about 1 mGy. In estimating risks of leukemias, the LQ model 

described above is assumed to apply in all cases of acute exposure (i.e., at any dose), and only the linear 

term in the modeled dose-response for acute exposure is assumed to apply in cases of chronic exposure. 

The main purpose of this study was to evaluate the scientific basis for developing DDREFs and to 

provide NIOSH with a recommendation on revising the probability distributions of DDREFs for solid 

cancers in IREP. More generally, our intent was to develop a probability distribution of a DDREF for 

solid cancers that could be used in any cancer risk assessments that account for uncertainty. We also 

evaluated the adequacy of the LQ dose-response model for all leukemias (excluding CLL) and specific 

types of leukemia in IREP and application of the LQ model to chronic as well as acute exposures. This 

study involved a comprehensive review of microdosimetric, radiobiological, and epidemiological data on 

low-dose and low-dose-rate extrapolations of cancer risks associated with exposure to low-LET radiation. 

 

3 In modeling risks of leukemias in the LSS cohort, the LQ dose-response model is of the form α(Dγ + 10Dn) + βDγ

2

, 

where Dγ and Dn are the absorbed doses from photons and neutrons, respectively, and the dose-response from 

neutrons (high-LET radiation) is assumed to be linear. The weighted dose from neutrons (10Dn) is small compared

with the dose from photons and has little effect on the dependence of the implicit DDREF on dose.
xx

USE OF LINEAR-QUADRATIC DOSE-RESPONSE MODEL TO ESTIMATE DDREFS

Use of a DDREF in estimating cancer risks at low doses or low dose rates of low-LET radiations

has been based mainly on an assumption that the dose-response from acute exposure to those radiations is 

inherently LQ in form, even when dose-responses for cancer in humans appear to be essentially linear. 

This assumption was based largely on acute dose-responses for various endpoints in cells, especially 

induction of dicentric chromosome aberrations, that appeared to be LQ in form. Since the response per 

unit dose in an LQ model decreases with decreasing dose, there was a perceived need to apply a reduction 

factor in estimating cancer risks in humans using a linear dose-response model at doses in the essentially

linear (low-dose) region of an LQ model that are below limits of epidemiological detection (e.g., at doses 

to the colon less than about 100 mGy for all solid cancers the LSS cohort). As noted above, an LQ model 

with an implicit DDREF >1 is often used to represent an observed non-linearity in the dose-response for 

all leukemias in the LSS cohort. Although dose-responses for solid cancers in the LSS cohort usually can 

be described by a linear model, an LQ model is often used to assess possible departures from linearity for 

the purpose of estimating a DDREF. However, a DDREF can be estimated for any functional form of a 

dose-response relationship; an assumption of an LQ dose-response model is not required.

Since microdosimetric considerations imply that the initial radiation damage at very low doses 

should be independent of dose rate, a DDREF that is estimated by analyzing possible non-linearities in 

acute dose-responses using an LQ model usually is assumed to represent a reduction in risks per unit dose 

at low dose rates as well. However, results from various radiobiological studies have suggested that the 

effects of dose protraction may not be adequately represented by a DDREF that is derived by assuming an 

LQ dose-response model for acute exposures and, further, that an LQ dose-response may not be a 

universal expectation for radiation carcinogenesis in either laboratory animals or humans. 

The basis for estimating a DDREF, especially the use of DDREFs that are derived from analyses of 

the curvature in acute dose-responses assuming an LQ model, is called into question by recent 

developments in radiation cytogenetics. Studies of chromosome aberrations using multifluor fluorescence

in situ hybridization (mFISH) indicated that most of the curvature in acute dose-responses that could be 

represented by an LQ model when aberrations were scored using conventional Giemsa staining was due 

to the competing influences of multiple endpoints with different dose-response relationships, none of 

which is LQ in form, rather than the curvature in an LQ dose-response for a single endpoint. Studies of 

chromosome aberrations using mFISH also showed that the apparently linear dose-response for simple 

aberrations depended on dose rate; i.e., the response per unit dose from chronic exposure was 

substantially less than the response per unit dose from acute exposure, contrary to expectations based on 

an LQ model that a linear dose-response should not depend on dose rate. 
xxi

A significant dependence on dose rate also was observed in some studies of cancer induction and 

life-span shortening in laboratory animals in which the dose-response from acute exposure appeared to be 

linear. Other studies of cancer induction in animals showed more complex dose-responses, such as U- or 

J-shaped dose-responses or a concave downward curvature at lower doses that suggested a supralinear 

dose-response (DDREF <1). These results do not conform to expectations based on an LQ model. 

We think that results from studies in cells and laboratory animals described above indicate that a 

DDREF for cancer in humans should not be estimated based solely on an analysis of non-linearities in 

dose-responses from acute exposure, such as an analysis of the curvature in dose-responses in the LSS 

cohort assuming an LQ model. Results from studies in cells and animals indicate that comparisons of

dose-responses from acute and chronic or protracted exposures also should be taken into account.

UNCERTAINTY IN LOW-DOSE RESPONSES DUE TO COMPLEXITY OF BIOLOGICAL 

SYSTEMS AND RESPONSE MECHANISMS 

It is generally recognized that induction of cancer by ionizing radiation is initiated by damage to 

cellular DNA, especially DNA double-strand breaks. However, studies of various phenomena reviewed in 

this report indicate that radiation carcinogenesis in humans is a complex, multistage process that may not 

be adequately represented by an LQ dose-response model that is essentially linear at low doses. 

Biophysical arguments for the LNT hypothesis at low doses are plausible only if single cells that 

are genetically altered by radiation act autonomously to produce a cancer. However, those arguments

were developed largely without knowledge of epigenetic factors, intercellular interactions, and 

homeostatic mechanisms that appear to play a significant role in radiation carcinogenesis. Current 

information also indicates that a tumor is a heterogeneous population of cells, with differing tumorigenic 

and metastatic potentials, not a homogeneous clone that is derived from a mutation or chromosome 

aberration induced in a single cell, as implied by biophysical arguments for an LNT model. 

RECENT CHALLENGES TO LNT MODEL

The importance of cellular and tissue- or organ-level responses to radiation in vivo and the extent to 

which those responses and their outcomes are different at low doses than at high doses is the subject of 

considerable debate and research at the present time. The assumption of an inherently LQ dose-response 

for low-LET radiation, with an essentially linear response at low doses or low dose rates and an implied 

DDREF >1, has been challenged in many ways. 

Some investigators (e.g., UNSCEAR 2008) cited results from a variety of studies, including 

modeling of data in the LSS cohort and results from epidemiological studies involving chronic exposure, 
xxii

as evidence for linearity in the dose-response for cancer over a large dose range, with no reduction in 

risks at low doses or low dose rates (DDREF ≡ 1). Others (e.g., Little et al. 1999; UNSCEAR 2000) 

argued that the dose-response for some cancer types may have a threshold (DDREF = ∞). Still others

(e.g., Snyder 2003; Hooker et al. 2004) believe that the LQ model underestimates risks at low doses, 

based on observations of bystander effects, inverse dose-rate effects, and suggestions of supralinearity in 

dose-responses in some epidemiological studies (DDREF <1), and that use of a DDREF >1 with an LNT 

model serves to exacerbate an underestimation of cancer risks. Proponents of hormesis (e.g., Calabrese 

and Baldwin 2001, 2003; Feinendegen 2005) cite evidence that there are beneficial effects of radiation 

exposure at low doses or low dose rates, including a reduction of adverse health effects. The French 

national academies (Aurengo et al. 2005) rejected the biophysical argument for the LNT hypothesis and 

argued that different biological mechanisms are active at low doses, such that the LNT model greatly 

overestimates risks at doses of low-LET radiation <100 mGy and even more so at doses <10 mGy.

Our re-evaluation of DDREFs currently used in IREP assumes that an LNT dose-response model 

for cancer will continue to be used in IREP and other cancer risk assessments. However, recent data on 

adaptive responses, non-targeted and delayed effects (e.g., bystander effects, genomic instability), and 

other phenomena raise the possibility that the form of the dose-response at low doses or low dose rates is 

highly uncertain, and that a simple linear extrapolation from higher doses, even including a DDREF, may 

not be appropriate. Although basic knowledge of these phenomena is increasing rapidly, the extent to 

which they affect cancer induction in humans at low doses remains largely a matter of speculation. A 

better understanding of the underlying mechanisms, the extent to which they are active in vivo, and how 

they are interrelated is needed before they can be incorporated quantitatively into methods of estimating 

cancer risks in humans at low doses or low dose rates. We think that this lack of understanding leads to a 

greater uncertainty in DDREFs than is represented by probability distributions derived from an analysis of 

dose-responses from acute exposure in the LSS cohort. 

GENERAL CONSIDERATIONS IN RE-EVALUATING DDREFS IN IREP

A re-evaluation of the probability distributions of DDREFs for solid cancers currently used in IREP 

is worthwhile when the uncertainty in a DDREF is often one of the largest contributors to uncertainty in 

estimates of cancer risks and PC/AS at low doses or low dose rates. If dose-responses that are modeled on 

the basis of data in the LSS cohort were fully representative of cancer risks at low doses (e.g., <10 mGy) 

and low dose rates, a DDREF might not be needed, given that most investigators concluded that there is 

little evidence for a departure from linearity in dose-responses for most solid cancers in the LSS cohort. 
xxiii

However, estimated risks of solid cancers at low doses based on analyses of data in the LSS cohort 

have large uncertainties, such that the slope of modeled risks at low doses is significantly greater than 

zero only when data in groups of survivors with doses to the colon up to 125–250 mGy are pooled. And, 

contrary to expectations based on an LNT model, the modeled risk based on data over this dose range 

may be higher than an estimated risk based on data over a wider dose range (e.g., 0–2 or 0–4 Gy), which 

suggests a supralinear dose-response at the lowest doses (DDREF <1). The unresolved question is 

whether the apparently linear dose-responses for most solid cancers in the LSS cohort over these wider 

dose ranges could still conceal some degree of dependence on dose or dose rate (e.g., a supralinear 

response) or justify the application of a DDREF >1 at low dose rates, if not at low acute doses. 

To some extent, this question is related to the issue of how a low dose or low dose rate should be 

defined. As part of this study, microdosimetric, radiobiological, and epidemiological data that could be 

used to define a dose or dose rate below which a DDREF should be applied were reviewed. The available 

data suggest that the upper limit of the log-uniform probability distribution of the uncertain parameter DL

that is used in IREP to determine when a DDREF is applied in estimating cancer risks from acute 

exposure to low-LET radiation should be maintained at 200 mGy, but that the lower limit should be 

reduced from 30 to 10 mGy; i.e., a “low” acute dose should be defined as any dose less than an uncertain 

value DL in the range of 10–200 mGy. 

A parameter to represent uncertainty in defining a low dose rate from exposure to low-LET 

radiation, similar to the uncertain parameter DL to represent the upper limit of a low acute dose, is not 

used in IREP. We think that available data do not support the development a probability distribution to 

represent uncertainty in the dose rate below which a DDREF should be applied. An exposure often is 

considered to be chronic when the dose rate averaged over a period of a few hours is less than 6 mGy h−1. 

The term “DDREF” embodies two distinct concepts: (1) a low-dose effectiveness factor (LDEF), 

which is estimated by analyzing possible non-linearities in dose-responses from acute exposure, such as a 

dose-response for solid cancers in the LSS cohort; and (2) a dose-rate effectiveness factor (DREF), which 

is estimated by comparing dose-responses from acute and chronic or protracted exposures. Most 

epidemiological data that have been used to estimate a DDREF, such as dose-responses in the LSS cohort,

provide estimates of an LDEF. However, comparisons of dose-responses in populations that received 

chronic or protracted exposures (e.g., radiation workers or medical patients) with dose-responses in the 

LSS cohort can provide estimates of a DREF. Since microdosimetric and other theoretical considerations 

imply that radiation effects at doses of about 0.1–1 mGy or less should be independent of dose rate, the 

separate concepts of an LDEF and a DREF usually have been combined into a DDREF. Nonetheless, we 

have distinguished between LDEFs and DREFs in evaluating radiobiological and epidemiological data. 
xxiv

EVALUATIONS OF DATA TO ESTIMATE DDREFS

Evaluations of Data from Radiobiological Studies

As part of this study, data on radiation dose-responses in cells and laboratory animals that might be 

relevant to estimating DDREFs for cancer in humans were evaluated.

Genetic and cytogenetic data. We reviewed data on the dependence of radiation dose-responses 

on dose and dose rate from studies of genetic and cytogenetic endpoints, including somatic mutations, cell 

transformation, and a variety of chromosomal aberrations. Although these model systems often are

considered to be simple, interpretation of the data can be difficult. More importantly, it is questionable 

whether data for various endpoints in cells can be extrapolated to cancer induction in humans. 

Most LDEFs and DREFs based on dose-responses for genetic and cytogenetic endpoints are in the 

range of 1–10. However, some data suggest values <1 or >10 including ∞ from potentially hormetic or 

apparently threshold responses.4 A central estimate suggested by these data is in the range of about 2–6. 

Use of data in cells to estimate a DDREF is based on biophysical (e.g., microdosimetric) arguments 

for the validity of an LNT model at doses below those at which statistically significant responses have 

been observed in humans. Although these arguments are considered to be plausible by many expert 

groups, they depend on assumptions about the ability of single cells to act autonomously in producing a 

cancer, assumptions which are now questionable. 

Cancer induction in animals. Data on induction of several cancer types in laboratory animals 

exhibit varied dose-responses, most commonly linear, LQ, or threshold but also including supralinear or

hormetic responses. Apparent thresholds typically occur when certain organs or tissues are targeted (e.g., 

bone, lung, or skin). Interpretation of dose-responses in animals (and humans as well) is often 

complicated by the use of different energies of low-LET radiations in different studies (e.g., 60Co gamma 

rays or 180–250 kVp x rays) and an uncertain dependence of the biological effectiveness of low-LET 

radiations on energy. The frequent assumption that high-energy gamma rays and lower-energy x rays 

have the same biological effectiveness does not conform to the current state of knowledge.

The most abundant data from studies in laboratory animals are DREFs for a variety of solid tumors 

and hematopoietic cancers in rodents. Consideration of the effects of dose rate and dose fractionation on 

dose-responses expands the range of plausible values of a DDREF compared with using estimates derived 

from analyses of acute dose-responses only. 

 

4 A DDREF for a potentially hormetic response (i.e., a response that falls below the level of controls) should have a 

finite, negative value. However, since risk coefficients (ERRs per unit dose) in IREP are constrained to be ≥0 based 

on the assumption of an LNT model, we assigned a DDREF of ∞ to represent hormetic or threshold dose-responses.

An apparent threshold in a dose-response may not mean that there is a dose below which there is no response.
xxv

Data in laboratory animals have several disadvantages, however. For a variety of biological and 

methodological reasons, DDREFs derived from studies in animals vary greatly and are difficult to relate 

to cancer induction in humans, even when analyses are restricted to data on tumor types relevant to 

humans. The range of ages at exposure that have been studied is limited, and there are concerns about 

extrapolating responses in laboratory animals to humans (e.g., because of genetic differences and the 

genetic uniqueness of highly inbred animal strains). Some dose-responses are complex and difficult to 

interpret. In particular, effects of cell sterilization and hormonal influences appear to be important for 

some cancer types. Although DREFs for osteosarcomas can be estimated from studies of protracted 

exposures in beagle dogs, dose-responses exhibit thresholds, which limits their utility compared with 

most of the other data on DREFs for which central estimates and confidence intervals are finite.

As discussed previously, risks of leukemias (excluding CLL) from exposure to low-LET radiation 

are estimated in IREP by assuming an LQ dose-response model under conditions of acute or highly 

fractionated exposures or a linear model under conditions of chronic or protracted exposures. On the 

whole, however, data from studies of leukemias in laboratory animals do not appear to provide support 

for an LQ dose-response from acute exposures or a linear dose-response at low dose rates. 

Life shortening in animals. Although life shortening in animals at low-to-intermediate doses 

(<3 Gy) and low dose rates is predominantly attributable to an accumulation of malignancies, we 

concluded that, with the exception of studies noted in the summary below, data on dose-responses for this 

endpoint should not be considered for use in estimating DDREFs for solid cancers. The range of central 

estimates of DREFs (about 1–13) based on dose-responses for life shortening in laboratory animals is 

similar to the range based on studies of cancer induction in animals. However, the spectrum of tumor 

types induced by protracted exposures in laboratory animals (e.g., mice) at low dose rates is different 

from the spectrum of tumor types induced by acute exposures. More hematopoietic cancers, which occur 

earlier and exhibit a higher level of lethality than solid tumors, are induced by acute exposures, whereas 

protracted low-dose-rate exposures of mice yield more lymphomas and ovarian tumors later in life, with 

much less loss of life span. In addition, not all tumors are a cause of life shortening in animals or humans. 

Thus, we concluded that DREFs derived from analyses of dose-responses for life shortening in laboratory 

animals have limited utility for the purpose of estimating a DDREF for solid cancers. 

Summary of data from studies in animals. Most DDREFs based on analyses of dose-responses 

for solid tumors in laboratory animals are in the range of 1–15. However, some analyses indicate a 

DDREF <1 and others indicate a DDREF of ∞ from threshold or potentially hormetic responses. A 

central estimate based on those analyses appears to be in the range of about 2–4. A DDREF of 1, which is 

at the lower end of the range of values, was estimated in studies of life shortening in which mortality due 

to leukemias and thymic lymphomas was excluded. In studies of bone cancer from protracted internal 
xxvi

exposures of dogs and mice to beta-emitting radionuclides, estimated lower limits on a DDREF in cases 

of non-zero responses that exhibited a threshold are in the range of 7–≥18. In a study of non-melanoma 

skin cancers from highly fractionated exposures in mice that also exhibited a possible threshold in the

dose-response, an estimated lower limit on a DDREF is about 30. 

Evaluations of Data from Epidemiological Studies

Radioepidemiological data on several types of cancers in humans can be used to estimate DDREFs. 

In this study, we evaluated data on dose-responses for all solid cancers as a group (incidence and 

mortality), female breast cancer (incidence and mortality), thyroid cancer (incidence), lung cancer 

(incidence and mortality), skin cancers (incidence), and leukemias (incidence and mortality). Data on 

bone cancer also were considered but were judged to be uninformative, due to the small number of cases 

in the LSS cohort (Preston et al. 2007) and lack of statistical significance of a dose-response in workers at 

the Mayak complex in Russia (Sokolnikov et al. 2008). 

Although dose-responses for many solid cancers in the LSS cohort and in radiation workers or 

medical patients are approximately linear, interpretation of some dose-responses is not straightforward. 

Some dose-responses for solid cancers in the LSS cohort show the effects of cell sterilization in reducing 

risks per unit dose at the highest doses. This effect is represented by a dose-response model of the form 

R = (αD + βD2

) exp(−γD). Hormonal influences appear to be important in interpreting the dependence of 

dose-responses for female breast cancer on age. Accounting for the interaction of smoking and radiation 

is important in estimating risks of lung cancer. Dose-responses for incidence of non-melanoma skin 

cancers and basal cell carcinoma in the LSS cohort clearly are non-linear. A threshold dose-response (an 

LDEF of ∞) cannot be excluded on the basis of data on leukemia mortality (excluding CLL) in the LSS

cohort or data on lung cancer mortality in tuberculosis fluoroscopy cohorts. UNSCEAR (2008) concluded 

that dose-responses for incidence of non-melanoma skin cancers and bone cancer in the LSS cohort are 

best represented by quadratic models, which incorporate an LDEF that approaches ∞ as the dose and dose 

rate approach zero. UNSCEAR (2008) also suggested that a quadratic dose-response model with an 

exponential cell-sterilization term may best describe leukemia mortality in the LSS cohort. Recent 

analyses of data in the LSS cohort (Richardson et al. 2009; Hsu et al. 2013) indicated that the apparently 

LQ dose-response for all leukemias (excluding CLL) is an artifact of combining an essentially quadratic 

dose-response for acute myeloid leukemia (AML) with approximately linear dose-responses for chronic 

myeloid leukemia (CML) and acute lymphocytic leukemia (ALL). Finally, uncertainties in the biological 

effectiveness of neutrons (high-LET radiation), for which dose-responses should be linear, can affect 

estimates of the curvature in dose-responses for solid cancers or leukemias in the LSS cohort, and 
xxvii

uncertainty in doses from neutrons and alpha particles can affect modeling of dose-responses from 

exposure to low-LET radiations in some radiation workers. 

On the basis of an assumption that linear dose-response models for solid cancers modified by a 

DDREF and an LQ dose-response model for all leukemias, which incorporates a dose-dependent DDREF 

implicitly, would continue to be used in IREP and other cancer risk assessments, epidemiological data for 

solid cancers and leukemias were evaluated separately in this study.

Evaluation of data for solid cancers. We evaluated a variety of data from epidemiological studies 

that can be used to estimate DDREFs for solid cancers. Those studies provided data to estimate LDEFs 

for incidence or mortality from all solid cancers or specific solid cancers, which usually were based on 

analyses of the curvature in dose-responses in the LSS cohort assuming an LQ model, or data to estimate 

DREFs for incidence or mortality from all solid cancers or specific solid cancers, which were based on 

comparisons of risks to radiation workers, medical patients, or members of the public that received 

chronic or protracted exposures with risks from acute exposure in the LSS cohort. 

DDREFs for all solid cancers or specific solid cancers that we derived from results of selected

epidemiological studies are shown in Figure ES.1; central values are estimated 50th percentiles, and 

uncertainties are 90% subjective confidence intervals (CIs). LDEFs and DREFs for all solid cancers in the 

top portion of Figure ES.1 were included in our analysis to estimate a DDREF for low-LET radiation. 

Those LDEFs and DREFs and their bases are summarized in Table ES.1.

LDEFs and DREFs for all solid cancers shown in Figure ES.1 and given in Table ES.1 were 

derived from results of studies of the LSS cohort in which dose-responses were analyzed using the DS02 

dosimetry system and neutron-weighted doses to the colon assuming a neutron RBE of 10; the previous 

DS86 dosimetry system with an assumed neutron RBE of 10 or 20 was used in several studies that were 

used to derive cancer-specific LDEFs or DREFs. Except for one analysis in which an LDEF for mortality 

from all solid cancers was based on estimates of excess absolute rates (EARs) in the LSS cohort, LDEFs 

or DREFs for all solid cancers were based on estimated ERRs in the study populations. LDEFs, DREFs, 

or DDREFs for breast and thyroid cancers were based on estimated ERRs and EARs, whereas LDEFs for

lung and skin cancers were based on estimated ERRs only. Except for the LDEFs for skin cancers, 

LDEFs were based on estimates of the curvature in an acute dose-response assuming an LQ model. 

The following points about some of the estimates in Figure ES.1 should be noted. 

• The LDEFs for solid cancer incidence or mortality are based on the most recent analyses of data

in the LSS cohort by various expert groups. Results from an analysis of solid cancer mortality 

based on DS02 dosimetry at RERF by Preston et al. (2004) are not included based on a judgment 

that those results are superseded by results from analyses, also at RERF, by Ozasa et al. (2012). 
xxviii

Figure ES.1. Estimates of 50th percentiles and 90% CIs of DREF, LDEF, or DDREF for all solid cancers or specific solid cancers based on 

selected epidemiological studies. Estimates are based on modeled ERRs and DS02 dosimetry in LSS cohort, except as noted. UK = United 

Kingdom workers; INWORKS = International Nuclear Workers Study; TB = tuberculosis fluoroscopy cohort; SH = skin hemangioma cohort; 

TC = tinea capitis cohort; MRH = Michael Reese Hospital cohort; LH = lymphoid hyperplasia cohort; BCC = basal cell carcinoma; * range of 

shielded kerma from photons and neutrons (neutron-weighted doses to colon for all solid cancers or identified organ otherwise).

0.1 1 10 100

Preston et al. (2007) LSS

BEIR VII LSS

BEIR VII LSS

UK vs LSS

Techa River vs LSS

Little et al. (2008) LQ LSS

Little et al. (2008) LQ EAR LSS

Ozasa et al. (2012) LSS

Ozasa et al. (2012) LSS

Ozasa et al. (2012) LSS

Ozasa et al. (2012) LSS

UK vs LSS

INWORKS vs LSS

Techa River vs LSS

Little & Muirhead (2000, 2004) LSS

Little & Muirhead (2000, 2004) LSS

(TB+SH) vs LSS

TB vs LSS

Veiga et al. (2016) LSS

Little & Muirhead (2000, 2004) LSS

(TC+MRH+LH+SH) vs LSS

Mayak vs LSS

Little & Muirhead (2000, 2004) LSS

Little & Muirhead (2000, 2004) LSS

Preston et al. (2007) BCC LSS

Non-melanoma LSS

DREF / LDEF / DDREF

ALL SOLID

CANCERS

BREAST

THYROID

SKIN

LUNG

DDREF

DREF

LDEF

Incidence Mortality

DS86

DS86

DS86

DS86

[(1+(β/α)]

[αL

/αLQ]

[(1+(β/α)]

[αL

/αLQ] } 0-2 Gy

[(1+(β/α)]

[αL

/αLQ] } 0-4 Gy*

DS86

DS86

0-4 Gy*

0-2 Gy

0-4 Gy*

DS86

0-4 Gy*

0-2 Gy
xxix

Table ES.1. Estimates of 50th percentiles and 90% CIs of LDEFs and DREFs for all solid cancers

included in analysis to develop probability distribution of DDREF

Factor Estimate Description

LDEF, incidencea 1.5 (0.9, 2.4)b

1.5 (1.0, 2.3) 

Calculated as αL/αLQ or [1 + (β/α)D] at 1 Gy based on 

analysis by BEIR VII committee (NRC 2006) of ERRs in 

LSS cohort at colon doses of 0–1.5 Gy 

1.4 (1.0, 1.90) Calculated as [1 + (β/α)D] at 1 Gy based on analysis by 

Preston et al. (2007) of ERRs in LSS cohort at colon doses 

of 0–2 Gy

LDEF, mortalitya 1.34 (1.01, 2.53) 

1.51 (1.07, 3.26) 

Calculated as [1 + (β/α)D] at 1 Gy based on analyses by 

Little et al. (2008) of ERRs or EARs in LSS cohort at colon 

doses corresponding to shielded kerma of 0–4 Gy

3.2 (1.2, 8.3)c

2.0 (1.0, 6.8) 

Calculated as [1 + (β/α)D] at 1 Gy or αL/αLQ based on 

analysis by Ozasa et al. (2012) of ERRs in LSS cohort at 

colon doses of 0–2 Gy

1.11 (0.94, 1.48) 

1.16 (0.77, 1.90) 

Calculated as [1 + (β/α)D] at 1 Gy or αL/αLQ based on 

analysis by Ozasa et al. (2012) of ERRs in LSS cohort at 

colon doses corresponding to shielded kerma of 0–4 Gy

DREF, incidenced

 1.4 (0.64, 5.9) Based on analyses of ERRs in U.K. radiation workers by 

Muirhead et al. (2009) and ERRs in LSS cohort by Jacob et 

al. (2009) 

0.63 (0.33, 2.2) Based on analyses of ERRs in Techa River cohort by Davis 

et al. (2015) and ERRs in LSS cohort by BEIR VII 

committee (NRC 2006)

DREF, mortalityd

 1.0 (0.39, 5.0) Based on analyses of ERRs in U.K. radiation workers by 

Muirhead et al. (2009) and ERRs in LSS cohort by Jacob et 

al. (2009) 

0.55 (0.30, 1.5) Based on analyses of ERRs in radiation workers in France, 

U.K., and U.S. (INWORKS) by Richardson et al. (2015) and 

ERRs in LSS cohort by BEIR VII committee (NRC 2006)

0.64 (0.31, 2.7) Based on analyses of ERRs in Techa River cohort by 

Schonfeld et al. (2013) and ERRs in LSS cohort by BEIR 

VII committee (NRC 2006) 

a LDEFs are estimated based on analyses of the curvature in modeled linear-quadratic (LQ) dose-responses in LSS 

cohort. Estimates of LDEF included in analysis represent approaches to modeling by BEIR VII committee (NRC 

2006), Radiation Effects Research Foundation (RERF) (Preston et al. 2007; Ozasa et al. 2012), and United Nations 

Scientific Committee on the Effects of Atomic Radiation (UNSCEAR) (Little et al. 2008).

b Estimate based on method of calculation preferred by BEIR VII committee NRC (2006). 

c Estimate based on curvature parameter (β/α) preferred by Ozasa et al. (2012).

d DREFs are estimated as ratios of age- and sex-matched estimates of ERR/Gy from acute exposure in LSS cohort 

assuming linear dose-response to estimates of ERR/Gy from protracted or chronic exposures of workers or Techa 

River cohort. Analyses of risks in LSS cohort by Jacob et al. (2009) were independent of analyses by BEIR VII 

committee (NRC 2006) and approaches to modeling dose-responses by other expert groups.
xxx

• LDEFs for solid cancer incidence based on analyses by the BEIR VII committee (NRC 2006) and 

LDEFs for solid cancer mortality based on analyses by Ozasa et al. (2012) indicate the 

dependence of LDEF on the method used to derive it—αL/αLQ vs [1 + (β/α)D] at 1 Gy, where αL

is the coefficient in a linear fit to the dose-response and αLQ is the coefficient of the linear term in 

a fit assuming an LQ model over the same range of doses. 

• Four LDEFs for solid cancer mortality based on analyses by Ozasa et al. (2012) are included in 

our analysis to estimate a DDREF for all solid cancers even though those investigators preferred 

the LDEF based on an estimate of the curvature parameter (β/α) at colon doses of 0–2 Gy. The 

other three LDEFs were estimated from central values of risk coefficients reported by Ozasa et al. 

(2012) and estimates of uncertainty in those coefficients provided by D. Preston (personal 

communication, November 6, 2016). 

• LDEFs for solid cancer incidence or mortality are based on analyses of dose-responses in the LSS 

cohort over various ranges of colon doses: 0–1.5 Gy (NRC 2006), 0–2 Gy (Preston et al. 2007; 

Ozasa et al. 2012), or a full dose range corresponding to a shielded kerma from photons and 

neutrons of 0–4 Gy (Little et al. 2008; Ozasa et al. 2012). The dependence of LDEF on the dose 

range over which a dose-response was analyzed is particularly apparent in estimates based on 

analyses by Ozasa et al. (2012).

• LDEFs for solid cancer mortality based on analyses by Little et al. (2008) indicate the dependence 

of LDEF on the measure of risk analyzed (ERR vs EAR). All other estimates of LDEF for solid 

cancer mortality or incidence were based on analyses of ERRs only. 

• We considered that the DREF for solid cancer mortality labeled “INWORKS vs LSS,” which was 

derived using an estimated risk to radiation workers in France, the U.K., and the U.S. (Richardson 

et al. 2015), superseded all DREFs that we derived using estimated risks of solid cancer mortality 

from the 15-country study of radiation workers (Cardis et al. 2007). Although DREFs we derived 

using estimated risks from the 15-country study with all or part of the Canadian cohort included 

were similar to the DREF we derived using an estimated risk from the INWORKS analysis, the 

validity of those DREFs is questionable, due to concerns about unreliable estimates of doses to 

some Canadian workers (Zablotska et al. 2014) and the importance of estimated risks to those 

workers to results from the 15-country study with all or part of the Canadian cohort included 

(Cardis et al. 2007). In addition, a DREF of 0.7 (−3.1, 4.5) we derived using an estimated risk 

from the 15-country study with the entire Canadian cohort excluded (Cardis et al. 2007), which 

we considered to be the most representative estimate from that study, has a substantially larger 

uncertainty with a 90% CI that overlaps zero and, thus, is largely uninformative compared with 

the DREF we derived using an estimated risk from the INWORKS analysis.
xxxi

• DREFs for solid cancer incidence and mortality we derived using estimated risks in the Techa 

River cohort (Schonfeld et al. 2013; Davis et al. 2015) were included in our analysis to estimate a 

DDREF despite concerns about the accuracy of estimated doses, which were based on modeling. 

We included those DREFs based in large part on the consideration that the Techa River cohort is 

the only cohort consisting of members of the public of all ages in which estimated risks of all 

solid cancers have been reported.

• In estimating the two DREFs for breast cancer incidence or mortality and the DDREF for thyroid 

cancer incidence labeled “(TC+MRH+LH+SH) vs LSS,” risks from exposure of medical patients 

to x rays were compared with risks from exposure to higher-energy photons in the LSS cohort by 

adjusting the former to account for an assumption of a greater biological effectiveness of medical 

x rays in inducing cancer in humans. Reported risks in medical patients were divided by a 

radiation effectiveness factor at low doses or low dose rates (REFL) of x rays with a central value 

of 2 and 90% CI of (1, 3); i.e., on average, the risk per unit dose from exposure to medical x rays 

was assumed to be twice the risk per unit dose from exposure to higher-energy photons. 

• Due to the varied conditions of exposure of medical patients in the tinea capitis (TC), Michael 

Reese Hospital (MRH), lymphoid hyperplasia (LH), and skin hemangioma (SH) cohorts, the entry 

for thyroid cancer incidence labeled “(TC+MRH+LH+SH) vs LSS” could not be identified as an 

LDEF or a DREF and is referred to as a DDREF.

• The DREF for lung cancer mortality labeled “Mayak vs LSS” was derived using an estimated risk 

in the LSS cohort that accounted for the joint effects of radiation and smoking (Furukawa et al. 

2010) and data on smoking status in workers at the Mayak complex (Sokolnikov et al. 2008). We 

concluded that estimated risks of lung cancer in tuberculosis fluoroscopy cohorts (Davis et al. 

1989; Howe 1995; UNSCEAR 2008) could not be used to estimate a DREF when CIs of those 

estimates overlapped zero and some CIs had unspecified lower limits. 

• LDEFs for incidence of basal cell carcinoma (BCC) or all non-melanoma skin cancers were not 

based on analyses of possible non-linearities in an assumed linear dose-response in the LSS 

cohort, in contrast to the approach to estimating all other LDEFs. Rather, LDEFs for these skin 

cancers were estimated based on linear-spline fits to the dose-responses with a knot at 1 Gy

(Preston et al. 2007). These LDEFs were calculated as the ratio of the slope of the dose-response 

at doses >1 Gy to the slope at doses <1 Gy. Consequently, these LDEFs would not apply if linear 

risk coefficients used in estimating risks of basal cell carcinoma or all non-melanoma skin cancers 

were based on dose-responses in the LSS cohort at doses of 0–1 Gy only. 
xxxii

Results of our analyses of data for all solid cancers shown in Figure ES.1 indicate the importance of 

taking into account DREFs that are based on comparisons of risks in populations that received chronic or 

protracted exposures with risks from acute exposure in the LSS cohort. Estimates of a DREF for all solid 

cancers expand the range of credible values of a DDREF considerably by giving substantial weight to 

values <1. A DDREF <1 is not apparent in LDEFs for all solid cancers that are based on analyses of the 

curvature in dose-responses in the LSS cohort assuming an LQ model when all central estimates are >1 

and most lower limits of 90% CIs are 1 or greater.

Evaluation of data for leukemias. The evaluation of epidemiological data on radiation-induced 

leukemias in this study served several purposes. The first was to investigate whether the LDEF of 2 at a 

dose to bone marrow of 1 Gy that is implicit in the LQ dose-response models for acute exposure in IREP 

is consistent with recent analyses of the curvature in dose-responses for all leukemias (excluding CLL) in 

the LSS cohort. We also considered whether the use in IREP of LQ dose-response models for specific 

types of leukemia (AML, CML, and ALL) is supported by recent data in the LSS cohort. 

Second, estimated risks of all leukemias at a dose to bone marrow of 1 Gy in the LSS cohort based 

on an LQ dose-response model were compared with estimated risks per Gy from chronic exposure of 

workers or members of the public based on linear dose-response models to estimate a DREF at 1 Gy. 

Those DREFs were compared with LDEFs based on data in the LSS cohort. 

Third, dose-responses for all leukemias from acute exposure in the LSS cohort at doses sufficiently 

low that the quadratic term in an LQ dose-response is unimportant were compared with dose-responses 

from chronic exposure of workers based on linear models to evaluate the validity of the assumption in 

IREP that risks of leukemias from chronic exposure can be estimated using only the linear term in an LQ 

dose-response model for acute exposure. 

Fourth, we considered whether an LDEF for all leukemias in the LSS cohort could be used to 

represent an LDEF for solid cancers, given that the curvature in the acute dose-response for leukemias in 

the LSS cohort should be affected to a lesser extent by contributions from neutrons, which should have a 

linear dose-response, due to the substantially lower biological effectiveness of neutrons in inducing 

leukemias compared with solid cancers.

Finally, we considered whether recent data in the LSS cohort and populations that received chronic 

or protracted exposures indicate that CLL is radiogenic. Although CLL usually is not considered to be 

radiogenic, CLL is assumed to be radiogenic in the version of IREP used by NIOSH. The risk model for 

CLL is based on data on dose-responses for lymphoma and multiple myeloma in the LSS cohort. 

Results of our evaluation of epidemiological data on radiation-induced leukemias are summarized 

as follows.
xxxiii

• Estimates of an LDEF at 1 Gy for all leukemias (excluding CLL) based on DS02 dosimetry in the 

LSS cohort and estimates of a DREF at 1 Gy for all leukemias based on comparisons of risks in 

the LSS cohort with risks in workers or risks in children exposed to gamma radiation in natural 

background are consistent with the LDEF of 2 at 1 Gy that is implicit in the LQ dose-response 

model for all leukemias in IREP. However, there is substantial uncertainty in estimates of LDEF 

or DREF at 1 Gy that may not be fully accounted for in the dose-response model for all leukemias 

in IREP, in which the coefficients of the linear and quadratic terms are assumed to be equal and 

no additional uncertainty is assigned to the curvature parameter (β/α).

• Recent analyses by Richardson et al. (2009) and Hsu et al. (2013) showed that the apparently LQ 

dose-response for all leukemias (excluding CLL) in the LSS cohort does not represent the forms 

of dose-responses for specific types of leukemia, including AML, which exhibits a quadratic 

dose-response with little evidence of linearity at the lowest doses, and CML and ALL, which 

exhibit approximately linear dose-responses. In contrast to the assumption in IREP of an LDEF at 

1 Gy of 2 for specific types of leukemia, a quadratic dose-response implies that LDEF approaches 

∞ at the lowest doses, and a linear dose-response implies an LDEF of 1. 

• Given that the apparently LQ dose-response for all leukemias (excluding CLL) from acute 

exposure in the LSS cohort is largely an artifact of combining dose-responses for specific types of 

leukemia, none of which is LQ in form, estimates of an LDEF for all leukemias should not be 

used to represent an LDEF for solid cancers.

• Comparisons of recent estimates of risks of all leukemias (excluding CLL) in the LSS cohort at 

doses sufficiently low that only the linear term in an assumed LQ dose-response is important with 

estimated risks from chronic exposure of workers are broadly consistent with the assumption in 

IREP that the linear term in an LQ dose-response from acute exposure can be used to estimate 

risks from chronic exposure. However, uncertainties in ratios of the two risks are large, and a firm 

conclusion is not warranted.

• A recent analysis of data in the LSS cohort by Hsu et al. (2013) showed evidence of a statistically 

significant linear dose-response for CLL, which suggested that the risk of CLL might be increased 

at higher doses. However, the analysis was based on only 12 cases in the LSS cohort, and Hsu et 

al. (2013) cautioned that generalization of this finding to other populations may be unwarranted. 

Studies of populations that received chronic or protracted exposures are inconclusive on the 

question of whether CLL is radiogenic.
xxxiv

RECOMMENDATION ON PROBABILITY DISTRIBUTION OF DDREF FOR SOLID 

CANCERS

The primary focus of this study was to re-evaluate the probability distributions of DDREFs for 

breast and thyroid cancers or all other solid cancers that are currently used in IREP to estimate risks from 

exposure to low-LET radiation and to develop a recommendation for revising those distributions. 

Conclusions based on our evaluation of data for leukemias are summarized above. 

Assumptions and Initial Conclusions

Development of a recommendation on revising the probability distributions of DDREFs for solid 

cancers currently used in IREP was based on an assumption that an LNT dose-response model would 

continue to be used in estimating risks of solid cancers. With this assumption, an uncertain DDREF is 

used to adjust estimated risks of solid cancers per unit dose at high doses and high dose rates of low-LET 

radiation to obtain estimates of risks per unit dose at low doses or low dose rates. 

An important initial conclusion from this study is that a probability distribution of DDREF for solid 

cancers should be developed on the basis of epidemiological data only. This conclusion was based on two 

considerations. First, we judged that use of radiobiological data in cells and laboratory animals to estimate 

a DDREF for solid cancers in humans is problematic, due to (1) the large uncertainties in DDREFs that 

were derived on the basis of radiobiological data, (2) the limited number of cancer types that have been 

studied in laboratory animals and the absence of data for all solid cancers combined, (3) difficulties in 

interpreting some dose-responses in cells and animals, and (4) unresolved questions about the relevance 

of DDREFs in cells and laboratory animals to induction of cancer in humans. Second, a substantial body 

of epidemiological data that can be used to estimate a DDREF for solid cancers has become available 

since IREP was developed. Especially important, in our view, is the availability of estimates of risks of all 

solid cancers in radiation workers or members of the public that received chronic or protracted exposures, 

which can be compared with risks in the LSS cohort to estimate a DREF.

We then concluded that results of our analyses of epidemiological data for all solid cancers and 

specific solid cancers shown in Figure ES.1 do not support the distinction in IREP between a DDREF for 

breast and thyroid cancers and a DDREF for all other solid cancers. Therefore, we developed a single 

probability distribution of DDREF that is intended to apply to all solid cancers.

Finally, we concluded that a probability distribution of DDREF for solid cancers should be 

developed on the basis of estimates of LDEF or DREF for all solid cancers only. Although estimates of 

LDEF, DREF, or DDREF for specific solid cancers shown in Figure ES.1 are generally consistent with 

estimates of LDEF or DREF for all solid cancers, many of the estimates for specific solid cancers are 
xxxv

more uncertain or incorporate the use of DS86 dosimetry in the LSS cohort. Therefore, we judged that the 

data for specific solid cancers would not substantially alter the range of credible values of a DDREF 

based on the data for all solid cancers. 

Development of Probability Distribution of DDREF for All Solid Cancers 

The probability distribution of a DDREF for all solid cancers developed in this report was based on 

the LDEFs and DREFs for all solid cancers shown in Figure ES.1 and summarized in Table ES.1. 

Estimates of LDEF or DREF were combined based on assumptions about the relative weights that should 

be given to those estimates to represent their relevance to estimation of a DDREF. Our assumptions in

combining LDEFs and DREFs to estimate a DDREF are summarized as follows.

• The three LDEFs for solid cancer incidence were combined by giving 25% weight to each of the 

two distributions based on an analysis by the BEIR VII committee (NRC 2006) and 50% weight 

to the distribution based on an analysis by Preston et al. (2007). These assumptions give equal 

weight to LDEFs from the BEIR VII report (NRC 2006) and Preston et al. (2007).

• The six LDEFs for solid cancer mortality were combined by giving 25% weight to each of the 

two distributions based on an analysis by Little et al. (2008), 15% weight to each of the two 

distributions based on an analysis by Ozasa et al. (2012) at colon doses of 0–2 Gy, and 

10% weight to each of the two distributions based on an analysis by Ozasa et al. (2012) at a 

shielded kerma of 0–4 Gy. These assumptions give equal weight to LDEFs from Little et al. 

(2008) and Ozasa et al. (2012). 

• The two DREFs for solid cancer incidence were combined by giving 80% weight to the 

distribution based on an analysis of risks to U.K. radiation workers by Muirhead et al. (2009) and 

20% weight to the distribution based on an analysis of risks in the Techa River cohort by Davis et 

al. (2015). 

• The three DREFs for solid cancer mortality were combined by giving 40% weight to the 

distribution based on an analysis of risks to U.K. radiation workers by Muirhead et al. (2009), 

40% weight to the distribution based on an analysis of risks to radiation workers in France, the 

U.K., and the U.S. (INWORKS) by Richardson et al. (2015), and 20% weight to the distribution 

based on an analysis of risks in the Techa River cohort by Schonfeld et al. (2013).

The result of combining the individual distributions of LDEFs or DREFs was two distributions of LDEF 

and two distributions of DREF. 
xxxvi

The assumption that lower weights should be given to the LDEFs for solid cancer mortality based 

on an analysis by Ozasa et al. (2012) at a shielded kerma of 0–4 Gy was based on the consideration that 

those investigators preferred an LDEF based on an analysis at colon doses of 0–2 Gy. The assumption 

that low weights should be given to the DREFs for solid cancer incidence or mortality based on analyses 

of risks in the Techa River cohort was based mainly on concerns about uncertainties in estimated doses.

Other concerns about estimated risks in that cohort are discussed in Section 5.2.2.3 of this report. 

The approach we used to combine individual probability distributions of LDEFs or DREFs for solid 

cancer incidence or mortality was to calculate weighted averages of those distributions using the relative 

weights given above. Assumptions about the form of probability distributions of individual LDEFs or 

DREFs or probability distributions of risk coefficients that we used to derive those LDEFs or DREFs, as 

well as details of the approach we used to calculate weighted averages of individual LDEFs or DREFs, 

are described in Sections 6.3.2.1 and 6.3.2.4 of this report. 

We did not combine individual probability distributions of LDEFs or DREFs by weighting each 

distribution by the reciprocal of its variance, as was done, for example, in an analysis by Jacob et al. 

(2009) to compare estimated risks of solid cancers in several cohorts of workers or members of the public 

with estimated risks in the LSS cohort. That approach would give greater weight to estimates with smaller 

uncertainties and lesser weight to estimates with larger uncertainties. For example, in combining the 

individual distributions of LDEF for solid cancer mortality, the greatest weight would be given to the 

LDEF based on an analysis by Ozasa et al. (2012) at a shielded kerma of 0–4 Gy and calculated as [1 + 

(β/α)D] at 1 Gy, rather than the relatively low weight of 10% we assumed. Our judgment that a 

reciprocal-variance approach to weighting of individual distributions of LDEF or DREF should not be 

used was based mainly on the consideration that this type of weighting is most appropriate when 

distributions are statistically independent. However, this condition is not met when all LDEFs and DREFs 

included in our analysis were based on much the same data in the LSS cohort (e.g., estimates of dose 

based on DS02 dosimetry, follow-up of rates of solid cancer incidence or mortality for similar periods).5

 

 

5 Differences in LDEFs for all solid cancers in Figure ES.1 and Table ES.1 that were estimated using DS02 

dosimetry in the LSS cohort are due to differences in several factors that affected analyses of a dose-response by the 

various investigators. These include, for example, differences in (1) the size of the LSS cohort, (2) the approach to 

modeling dose-responses by different expert groups, (3) the cancer types included in “all solid cancers,” (4) the 

response under study (mortality vs incidence of solid cancers), (5) the measure of risk that was analyzed (ERR vs 

EAR), (6) assumed uncertainties in estimated doses to survivors, (7) whether survivors with an estimated shielded 

kerma >4 Gy were included in a dose-response analysis, (8) the period of follow-up of survivors (1958–1998 for 

solid cancer incidence vs 1950–2000 or 2003 for mortality), (9) the range of doses over which the non-linearity in a 

dose-response was analyzed using an LQ model, (10) the approach to estimating an LDEF, as in the analyses by the 

BEIR VII committee (NRC 2006) and Ozasa et al. (2012), and (11) the assumed dependence of risks on age at 

exposure and attained age or time since exposure. These differences also can affect estimates of DREF.
xxxvii

• The combined LDEFs and DREFs for solid cancer incidence should be given substantially greater 

weight than the combined LDEFs and DREFs for solid cancer mortality. Relative weights of 2:1 

were assigned to incidence- and mortality-based estimates of the combined LDEFs and DREFs.

The result is a single distribution of LDEF and a single distribution of DREF that represent estimates for 

solid cancer incidence and mortality combined. Our judgment that substantially greater weight should be 

given to LDEFs and DREFs for solid cancer incidence was based on several considerations: (1) accuracy 

of disease ascertainment is a greater concern in estimating risks of cancer mortality; (2) cancer mortality, 

but not incidence, can depend on the level and intensity of medical treatment; (3) estimates of mortality 

generally are less reliable for cancers that usually are non-fatal (e.g., thyroid cancer); and (4) use of 

LDEFs and DREFs based on data on cancer incidence is compatible with modeling of risks of cancer 

incidence in IREP. However, we also judged that substantial weight should be given to LDEFs and 

DREFs for solid cancer mortality. 

• Estimates of LDEF and DREF that were obtained by combining estimates for solid cancer 

incidence and mortality were given equal weight in estimating a probability distribution of 

DDREF for all solid cancers. 

Although there could be unknown biases and complicating factors in estimating DREFs by comparing 

risks of solid cancers from chronic or protracted exposures in radiation workers or members of the public 

with age- and sex-matched risks from acute exposure in the LSS cohort, there also are concerns that 

extrapolations of observed risks at higher acute doses (e.g., >1 Gy) in the LSS cohort to lower doses 

where risks are not observable (e.g., using an LQ dose-response model) may not be reliable.

• The probability distribution of DDREF for all solid cancers obtained as summarized above was 

truncated by removing values less than 0.2 and greater than 20. 

Truncation of the probability distribution of DDREF was based on our judgment that the weight of 

evidence from all the data in humans and the data in animals discussed in Section 4.3 of this report, 

assuming a linear no-threshold dose-response for cancer in humans, is that a DDREF for all solid cancers 

outside the range of 0.2–20 is not credible. However, truncation removed only about 1.3% of the values in 

the DDREF distribution without truncation and had only a small effect on estimated CIs. 

Selected percentiles of the probability distribution of a DDREF for all solid cancers that was 

developed on the basis of the assumptions summarized above and the lognormal probability distribution 
xxxviii

that provides the best fit to that distribution are given in Table ES.2. The best-fit lognormal distribution, 

which should be suitable for general use in cancer risk assessments that account for uncertainty, gives a 

good fit to our probability distribution. Deviations of the best-fit lognormal distribution from our 

distribution are most pronounced at the very lowest percentiles (below about 0.2) and at percentiles above

the 95th, where the lognormal distribution underestimates our DDREF distribution; underestimation of a 

DDREF results in overestimation of risks and PC/AS of diagnosed cancers. Lower values in a DDREF

distribution are the more important to NIOSH when upper 99th percentiles of uncertain estimates of ERR 

and PC/AS calculated in IREP are used in adjudicating claims for compensation for cancer (DHHS 2002). 

Table ES.2 also gives the harmonic mean of the probability distribution of DDREF developed in 

this report and the best-fit lognormal distribution. Because DDREF is a divisor in an equation to estimate 

cancer risks, the arithmetic mean of an uncertain estimate of risk, which is an important and commonly 

used measure of central tendency, is proportional to the reciprocal of the harmonic mean of DDREF, 

rather than the reciprocal of the arithmetic mean. For example, using the harmonic mean of the DDREF 

distribution in Table ES.2, the arithmetic mean of an estimated risk of solid cancers per unit dose at low 

doses or low dose rates is 1/1.1 = 0.91 times the arithmetic mean of an estimated risk per unit dose at high 

acute doses. This reduction in mean risks is rather modest (about 10%). Use of the reciprocal of the 

arithmetic mean of DDREF would underestimate the arithmetic mean of risks per unit dose at low doses 

or low dose rates. 

Estimates of LDEFs and DREFs for all solid cancers that were used in our analysis and a 

comparison of the probability distribution of DDREF for all solid cancers developed in this report with 

probability distributions developed by Jacob et al. (2009) and the BEIR VII committee (NRC 2006) are 

shown in Figure ES.2. Also shown is the probability distribution for solid cancers other than breast and 

thyroid currently used in IREP. 

Table ES.2. Summary of probability distribution of DDREF for all solid cancers developed in this 

report and lognormal distribution that gives best fit to DDREF distribution

Distribution Percentile of probability distribution 

2.5th 5th 50th 95th 97.5th 

Harmonic 

meana

DDREF distribution 0.39 0.47 1.3 3.6 5.6 1.1 

Best-fit lognormal distribution 

(GM = 1.31, GSD = 1.80)b

0.41 0.50 1.3 3.4 4.2 1.1 

a When probability distribution of DDREF is used in cancer risk assessments, arithmetic mean of uncertain estimate 

of risk at low doses or low dose rates is proportional to reciprocal of harmonic mean of DDREF.

b GM = geometric mean; GSD = geometric standard deviation.
xxxix

Figure ES.2. Estimates of 50th percentiles and 90% CIs of DREF or LDEF for solid cancer incidence or mortality used to develop probability 

distribution of DDREF for all solid cancers in this report (top) and comparison of our preferred distribution with DDREF distributions developed 

by Jacob et al. (2009) and BEIR VII committee (NRC 2006) and DDREF distribution for most solid cancers currently used in IREP (bottom). 

Distributions at top of figure are given in Figure ES.1. * Range of shielded kerma from photons and neutrons; range of neutron-weighted doses to 

colon in analyses by Ozasa et al. (2012) otherwise.

0.1 1 10 100

Preston et al. (2007) LSS

BEIR VII LSS

BEIR VII LSS

UK vs LSS

Techa River vs LSS

Little et al. (2008) LQ LSS

Little et al. (2008) LQ EAR LSS

Ozasa et al. (2012) LSS

Ozasa et al. (2012) LSS

Ozasa et al. (2012) LSS

Ozasa et al. (2012) LSS

UK vs LSS

INWORKS vs LSS

Techa River vs LSS

This Report

BEIR VII

IREP

DREF / LDEF / DDREF

ALL SOLID

CANCERS

3 incidence studies

7 mortality studies

X

X

X

X

X

Jacob et al. (2009)

Incidence Mortality

DREF

LDEF

INDIVIDUAL STUDIES

SYNTHESIS STUDIES

[(1+(β/α)]

[αL

/αLQ]

[(1+(β/α)]

[αL

/αLQ] 0-2 Gy

[(1+(β/α)]

[αL

/αLQ] } 0-4 Gy*

}
xl

In analyses by Jacob et al. (2009) summarized in Figure ES.2, risks of solid cancer mortality or 

incidence in several cohorts of workers or members of the public that received chronic or protracted 

exposures at low doses were compared with risks in the LSS cohort. Because CIs of estimated risks in 

several worker cohorts overlapped zero, Jacob et al. (2009) calculated ratios of risks to workers or 

members of the public to risks in the LSS cohort, which were referred to as “risk ratios”; a risk ratio is the 

reciprocal of a DREF. Risk ratios based on results from individual studies were combined by weighting 

each risk ratio by the reciprocal of its variance. Three combinations of risk ratios were calculated: (1) one 

for cancer mortality that was obtained by combining results from seven studies with a larger number of 

cancer cases, which Jacob et al. (2009) considered to be their main result; (2) one for cancer mortality that 

was obtained by combining results from four studies with a smaller number of cancer cases; and (3) one 

for cancer incidence that was obtained by combining results from the three studies of that endpoint. The 

results shown in Figure ES.2 are reciprocals of the reported central values and 90% CIs of two of the

combinations of risk ratios; the risk ratio based on four studies of cancer mortality is not shown. 

If a risk ratio (RR), as defined by Jacob et al. (2009), were used in cancer risk assessments, risks

per unit dose at low doses or low dose rates of low-LET radiation would be estimated as RL = RH × RR. 

We think that use of a risk ratio in cancer risk assessments that account for uncertainty has certain 

advantages over use of a DDREF, including that (1) the arithmetic mean of a probability distribution of 

RL is proportional to the arithmetic mean of a risk ratio, but is not proportional to the reciprocal of the 

arithmetic mean of DDREF, and (2) probability distributions of DREFs based on ratios of risks in the 

LSS cohort to risks in cohorts that received chronic or protracted exposures include a value of infinity 

when the CI of the risk from chronic or protracted exposure overlaps zero and, thus, are unstable. The 

latter concern led Jacob et al. (2009) to calculate risk ratios, rather than DDREFs, in their analyses. 

The probability distribution of DDREF developed by the BEIR VII committee (NRC 2006) was 

based mainly on an analysis of the curvature in the acute dose-response for solid cancer incidence in the 

LSS cohort, which gives an LDEF. The probability distribution based on the committee’s analysis of data 

in the LSS cohort was modified slightly by taking into account into account data in laboratory animals. 

We note the following points about the DDREFs shown at the bottom of Figure ES.2. 

• Substantial weight (nearly 30%) is given to an assumption that the risk of solid cancers per unit 

dose at low doses or low dose rates of low-LET radiation is greater than the risk per unit dose at 

higher acute doses in the LSS cohort. Since LDEFs based on analyses of possible non-linearities 

in dose-responses in the LSS cohort generally are >1, this property of our probability distribution 

is a consequence of including DREFs for solid cancer incidence or mortality that were based on 

comparisons of risks to workers or members of the public with risks in the LSS cohort. We think 
xli

that a credible estimate of a DDREF for solid cancers must take into account estimates of risks 

from chronic or protracted exposures that suggest a DDREF <1. 

• The probability distribution of DDREF developed in this report includes higher values than the 

distributions based on analyses by Jacob et al. (2009), whereas the latter distributions give greater 

weight to values <1. The main reason for these differences is that the analyses by Jacob et al. 

were based on comparisons of risks to workers or members of the public that received chronic or 

protracted exposures at low doses with risks in the LSS cohort only. 

• The probability distribution of DDREF developed by the BEIR VII committee (NRC 2006) did 

not take into account risks to workers or members of the public that received chronic or protracted 

exposures. Consequently, the BEIR VII distribution gives only a small weight to values <1. 

• The probability distribution of DDREF developed in this report and the distribution for all solid 

cancers excluding breast and thyroid currently used in IREP have similar 50th percentiles (1.3 vs 

1.5), but our distribution is substantially broader. The 90% CI of the DDREF for breast and 

thyroid cancers currently used in IREP is the same the 90% CI of the distribution for all solid 

cancers other than breast and thyroid shown in Figure ES.2. 

We reiterate that the probability distribution of DDREF for all solid cancers developed in this 

report is intended to be applied in estimating risks of specific solid cancers at low doses or low dose rates 

of low-LET radiation only when a linear dose-response from acute exposure over a wide range of doses, 

e.g., at doses in the LSS cohort up to about 2 Gy or higher, is assumed. If a non-linear dose-response from 

acute exposure is assumed, such as the linear-spline dose-responses for non-melanoma skin cancers and 

basal cell carcinoma developed by Preston et al. (2007) and the quadratic dose-response for bone cancer 

developed by UNSCEAR (2008), our DDREF for all solid cancers would not apply. 

The probability distribution of DDREF for all solid cancers developed in this report gives 

substantially greater weight to values <1 than the two distributions for solid cancers currently used in 

IREP. Consequently, 99th percentiles of uncertain estimates of ERRs and PC/AS used in adjudicating 

claims for compensation for cancer would increase if our distribution replaced the probability 

distributions of DDREF currently used in IREP. We emphasize, however, that it was not our intent to 

develop a probability distribution of DDREF that would be biased toward overestimation, or 

underestimation, of ERRs and PC/AS. Rather, our intent from the outset was to develop, on the basis of a 

review of available information, a probability distribution of DDREF for all solid cancers that is an 

unbiased representation of the current state of knowledge.